The Process of Model Building and Simulation of Ill-Defined Systems: Application to Wastewater Treatment

Kops, Sebastiaan; Vangheluwe, Hans; Claeys, Filip; Vanrolleghem, Peter A.; Yuan, Zhihong; Vansteenkiste, Ghislain

doi:10.1076/mcmd.5.4.298.3675

Cited by 7 publications

(5 citation statements)

References 15 publications

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“…In its most basic form, an experimental frame consists of a set of the conditions under which the system behaviour is to be observed, and two sets of variables, the system inputs and outputs, through which the system interacts with the surrounding environment (Kops et al 1999). The concept of experimental frame refers to a limited set of circumstances under which a system is to be observed or subjected to experimentation, i.e.…”

Section: Experimentationmentioning

confidence: 99%

“…In such cases, the problems can be evaluated using a computer simulation which is defined as the use of a computer software to predict the performance of a real system under different conditions. generates behaviour (Kops et al 1999). In a computer simulation, a model is evaluated numerically, and data are gathered in order to estimate the desired true characteristics of the model (Law and Kelton 2000).…”

Section: Modelmentioning

confidence: 99%

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Mathematical Modelling and Computer Simulation of Activated Sludge Systems

Mąkinia¹

2010

wio

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Section: Experimentationmentioning

confidence: 99%

Section: Modelmentioning

confidence: 99%

Mathematical Modelling and Computer Simulation of Activated Sludge Systems

Mąkinia¹

2010

wio

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“…Despite the increasing use of models in this area, reliable models of wastewater treatment processes are still relatively difficult to design and apply. The reason is that wastewater treatment processes are poorly defined systems, for which a valid model structure is not known and the model parameter values cannot be accurately specified (Kops et al, 1997). Difficulties in modeling wastewater treatment processes lie in quantification of the various complex biochemical reactions involved, the difficulty in gathering all of the required identification data, the large number of parameters and state variables typically included in model structure, model equations expressing high nonlinearities, and the fact that not all model parameters can be uniquely identified from plant measurements (Sperling, 1993).…”

Section: Introductionmentioning

confidence: 99%

Design of a Sequencing Batch Reactor Sequence with an Input Load Partition in a Simulation‐Based Experimental Environment

Hvala

Zec²,

Roš³

et al. 2001

Water Environment Research

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A study is presented that considers optimization of a sequencing batch reactor (SBR) operational sequence. Optimization is performed with the aid of a laboratory pilot plant and a process model. The model was included in the study to facilitate a search for optimal operating conditions because performing experiments solely on the pilot plant would be rather extensive, expensive, and time-consuming. The model used in the study is the well-known Activated Sludge Model No. 1 with minor modifications. The optimization addresses a split-feed operating mode with input load partitioned into two parts and with two successions of aerobic-anoxic phases. In the study, the duration of batch phases and the time of the second input addition are optimized so that the desired effluent nitrogen concentration is achieved and the need for external carbon addition in the anoxic phase is reduced or eliminated. When an optimal SBR sequence computed by the model was verified on the pilot plant, better performance of the actual process was also achieved, although some phenomena were observed that were not predicted by the model. Results of the study indicate that the available wastewater treatment plant models, although a simplification of reality, can be usefully applied for process optimization. Water Environ. Res., 73, 146 (2001).

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“…An MPI identifies a specific point of view by which a system can be analyzed according to a prevailing purpose. The quality of the analysis depends on the cognitive level and its related methodological approaches permitted by the knowledge maturity reached by the MPI itself (i.e., observable vs. not observable part [29,30]).…”

Section: Macrodomains Of Prevailing Interestmentioning

confidence: 99%

Reflections and Methodological Proposals to Treat the Concept of “Information Precision” in Smart Agriculture Practices

Mazzetto

Gallo

Sacco

2020

Sensors

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Smart Agriculture (SA) is an evolution of Precision Farming (PF). It has technological basis very close to the paradigms of Industry 4.0 (Ind-4.0), so that it is also often referred to as Agriculture 4.0. After the proposal of a brief historical examination that provides a conceptual frame to the above terms, the common aspects of SA and Ind-4.0 are analyzed. These are primarily to be found in the cognitive approaches of Knowledge Management 4.0 (KM4.0, the actual theoretical basis of Ind-4.0), which underlines the need to use Integrated Information Systems (IIS) to manage all the activity areas of any production system. Based upon an infological approach, “raw data” becomes “information” only when useful to (or actually used in) a decision-making process. Thus, an IIS must be always designed according to such a view, and KM4.0 conditions the way of collecting and processing data on farms, together with the “information precision” by which the production system is managed. Such precision needs, on their turn, depend on the hierarchical level and the “Macrodomain of Prevailing Interest” (MPI) related to each decision, where the latter identifies a predominant viewpoint through which a system can be analyzed according to a prevailing purpose. Four main MPIs are here proposed: (1) physical and chemical, (2) biological and ecological, (3) productive and hierarchical, and (4) economic and social. In each MPI, the quality of the knowledge depends on the cognitive level and the maturity of the methodological approaches there achieved. The reliability of information tends to decrease from the first to the fourth MPI; lower the reliability, larger the tolerance margins that a measurement systems must ensure. Some practical examples are then discussed, taking into account some IIS-monitoring solutions of increasing complexity in relation to information integration needs and related data fusion approaches. The analysis concludes with the proposal of new operational indications for the verification and certification of the reliability of the information on the entire decision-making chain.

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The Process of Model Building and Simulation of Ill-Defined Systems: Application to Wastewater Treatment

Cited by 7 publications

References 15 publications

Mathematical Modelling and Computer Simulation of Activated Sludge Systems

Mathematical Modelling and Computer Simulation of Activated Sludge Systems

Design of a Sequencing Batch Reactor Sequence with an Input Load Partition in a Simulation‐Based Experimental Environment

Reflections and Methodological Proposals to Treat the Concept of “Information Precision” in Smart Agriculture Practices

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